Relating digital assets using notable moments

ABSTRACT

Techniques of relating at least two digital assets based on digital asset management (DAM) are described. A DAM logic/module can obtain a knowledge graph metadata network (metadata network) of metadata associated with a collection of digital assets (DA collection). The metadata network comprises correlated metadata assets. Each metadata asset is represented a node in the metadata network. A correlation between metadata assets is represented as an edge in the metadata network. The DAM logic/module can select a first metadata asset using the metadata network. The DAM logic/module can also determine that the first metadata asset is associated with a second metadata asset. The DAM logic/module can identify a third metadata asset based on at least one of the first metadata asset or the second metadata asset. The DAM logic/module can cause one or more DAs associated with the third metadata asset to be presented via an output device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to the following applications: (i) U.S.Provisional Patent Application No. 62/349,109, entitled “USER INTERFACESFOR RETRIEVING CONTEXTUALLY RELEVANT MEDIA CONTENT,” Docket No.770003002400 (P31183USP1), filed Jun. 12, 2016; (ii) U.S. ProvisionalPatent Application No. 62/349,092, entitled “NOTABLE MOMENTS IN ACOLLECTION OF DIGITAL ASSETS,” Docket No. P31270USP1 (119-1249USP1),filed Jun. 12, 2016; (iii) U.S. Provisional Patent Application No.62/349,094, entitled “KNOWLEDGE GRAPH METADATA NETWORK BASED ON NOTABLEMOMENTS,” Docket No. P31270USP2 (119-1249USP2), filed Jun. 12, 2016; and(iv) U.S. Provisional Patent Application No. 62/349,099, entitled“RELATING DIGITAL ASSETS USING NOTABLE MOMENTS,” Docket No. P31270USP3(119-1249USP3), filed Jun. 12, 2016. Each of the above-referencedapplications is incorporated by reference in its entirety.

This application is related to the following applications: (i) U.S.Non-Provisional patent application Ser. No. ______, entitled “NOTABLEMOMENTS IN A COLLECTION OF DIGITAL ASSETS,” Docket No. P31270US1(119-1249US1), filed Dec. 27, 2016; (ii) U.S. Non-Provisional patentapplication Ser. No. ______, entitled “KNOWLEDGE GRAPH METADATA NETWORKBASED ON NOTABLE MOMENTS,” Docket No. P31270US2 (119-1249US2), filedDec. 27, 2016; and (iii) U.S. Non-provisional patent application Ser.No. 15/275,294, entitled “USER INTERFACES FOR RETRIEVING CONTEXTUALLYRELEVANT MEDIA CONTENT,” Docket No. 770002002400 (P31183US1), filed Sep.23, 2016. Each of these related applications is incorporated byreference in its entirety.

FIELD

Embodiments described herein relate to digital asset management (alsoreferred to as DAM). More particularly, embodiments described hereinrelate to determining relationships between digital assets (alsoreferred to as DAs) using a knowledge graph metadata network (alsoreferred to as a metadata network) generated based on one or morenotable moments in a collection of the digital assets (also referred toas a DA collection).

BACKGROUND

Modern consumer electronics have enabled users to create, purchase, andamass considerable digital assets (also referred to as DAs). Forexample, a computing system (e.g., a smartphone, a stationary computersystem, a portable computer system, a media player, a tablet computersystem, a wearable computer system or device, etc.) can store or haveaccess to a collection of digital assets (also referred to as a DAcollection) that includes hundreds or thousands of DAs (e.g., images,videos, music, etc.).

Managing a DA collection can be a resource-intensive exercise for users.For example, retrieving multiple DAs representing a sentimental momentin a user's life from a sizable DA collection can require the user tosift through many irrelevant DAs. This process can be arduous andunpleasant for many users. A digital asset management (DAM) system canassist with managing a DA collection. A DAM system represents anintertwined system incorporating software, hardware, and/or otherservices in order to manage, store, ingest, organize, and retrieve DAsin a DA collection. An important building block for at least onecommonly available DAM system is a database. Databases are commonlyknown as data collections that are organized as schemas, tables,queries, reports, views, and other objects. Exemplary databases includerelational databases (e.g., tabular databases, etc.), distributeddatabases that can be dispersed or replicated among different points ina network, and object-oriented programming databases that can becongruent with the data defined in object classes and subclasses.

One problem associated with using databases for digital asset management(DAM) is that the DAM system can become resource-intensive. That is,substantial computational resources may be needed to manage the DAs inthe DA collection (e.g., processing power for performing queries ortransactions, storage memory space for storing the necessary databases,etc.). This requirement may assist with reducing the processing poweravailable for other tasks. Another related problem associated with usingdatabases is that digital asset management (DAM) cannot be easilyimplemented on a computing system with limited storage capacity withoutmanaging the assets directly (e.g., a portable device such as asmartphone or a wearable device). Consequently, a DAM system'sfunctionality is generally provided by a remote device (e.g., anexternal data store, an external server, etc.) where copies of the DAsare stored and the results are transmitted back to the computing systemhaving limited storage capacity. Requiring external data stores and/orservers in order to use databases for managing a large DA collection canassist with making digital asset management (DAM) resource-intensive.This requirement may also assist with reducing the processing poweravailable for other tasks on the local device. At least one currentlyavailable DAM system uses metadata associated with a DA collection—suchas spatiotemporal metadata (e.g., time metadata, location metadata,etc.)—to organize DAs in the DA collection into multiple events. Thesecurrently available DAM system(s), however, organize the metadataassociated with the DA collection using databases, which can contributeto making digital asset management (DAM) a resource-intensive endeavoras explained above.

SUMMARY

Methods, apparatuses, and systems for determining relationships betweendigital assets (DAs) using a knowledge graph metadata network (alsoreferred to as a metadata network) that is generated based on one ormore notable moments in a collection of the digital assets (DAcollection) are described. Such embodiments can enable improved digitalasset management (DAM) without using traditional databases.

For one embodiment, a DAM logic/module obtains or generates a knowledgegraph metadata network (metadata network) associated with a collectionof digital assets (DA collection). The metadata network can comprisecorrelated metadata assets describing characteristics associated withdigital assets (DAs) in the DA collection. Each metadata asset candescribe a characteristic associated with one or more digital assets(DAs) in the DA collection. For a non-limiting example, a metadata assetcan describe a characteristic associated with multiple DAs in the DAcollection. Each metadata asset can be represented as a node in themetadata network. A metadata asset can be correlated with at least oneother metadata asset. Each correlation between metadata assets can berepresented as an edge in the metadata network that is between the nodesrepresenting the correlated metadata assets.

For one embodiment, the DAM logic/module identifies a first metadataasset in the metadata network. The DAM logic/module can also identify asecond metadata asset based on at least the first metadata asset. Forone embodiment, the DAM logic/module causes one or more DAs associatedwith the first and/or second metadata assets to be presented via anoutput device.

Other features or advantages attributable to the embodiments describedherein will be apparent from the accompanying drawings and from thedetailed description that follows below.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments described herein are illustrated by examples and notlimitations in the accompanying drawings, in which like referencesindicate similar features. Furthermore, in the drawings someconventional details have been omitted so as not to obscure theinventive concepts described herein.

FIG. 1A illustrates, in block diagram form, an asset managementprocessing system that includes electronic components for performingdigital asset management (DAM) in accordance with an embodiment.

FIG. 1B illustrates, in block diagram form, an exemplary knowledge graphmetadata network (also referred to as a metadata network) in accordancewith one embodiment. The exemplary metadata network illustrated in FIG.1B can be generated and/or used by the DAM processing system illustratedin FIG. 1A in accordance with an embodiment.

FIG. 2 is a flowchart representing an operation to perform DAM accordingto an embodiment.

FIG. 3A illustrates, in flowchart form, an operation to generate anexemplary metadata network in accordance with an embodiment.

FIGS. 3B-3C illustrate, in flowchart form, an operation to generate anexemplary metadata network in accordance with an embodiment. FIGS. 3B-3Cprovides additional details about the operation illustrated in FIG. 3A.

FIG. 3D illustrates, in flowchart form, an operation to generate one ormore edges between nodes in a metadata network in accordance with anembodiment. FIG. 3D provides additional details about the operationillustrated in FIGS. 3B-3C.

FIG. 4 is a flowchart representing an operation to relate and present atleast two digital assets (DAs) from a collection of DAs (DA collection)according to one embodiment.

FIG. 5 is a flowchart representing an operation to determine and presentat least two digital assets (DAs) from a DA collection based on apredetermined criterion in accordance with one embodiment.

FIG. 6 is a flowchart representing an operation to determine and presentrepresentative digital assets (DAs) for a moment according to oneembodiment.

FIG. 7 illustrates an exemplary processing system for DAM according toone or more embodiments described herein.

DETAILED DESCRIPTION

Methods, apparatuses, and systems for determining relationships betweendigital assets (also referred to as DAs) using a knowledge graphmetadata network (also referred to as a metadata network) that isgenerated based on one or more notable moments in a collection of thedigital assets (also referred to as a DA collection) are described. Suchembodiments can enable digital asset management (DAM) for the DAcollection without using traditional databases.

Embodiments set forth herein can assist with improving computerfunctionality by enabling computing systems that use one or moreembodiments of the metadata network described herein for digital assetmanagement (DAM). Such computing systems can implement DAM to assistwith reducing or eliminating the need to use databases for digital assetmanagement (DAM). This reduction or elimination can, in turn, assistwith minimizing wasted computational resources (e.g., memory, processingpower, computational time, etc.) that may be associated with usingdatabases for DAM. For example, DAM via databases may include externaldata stores and/or remote servers (as well as networks, communicationprotocols, and other components required for communicating with externaldata stores and/or remote servers). In contrast, DAM performed asdescribed herein can occur locally on a device (e.g., a portablecomputing system, a wearable computing system, etc.) without the needfor external data stores, remote servers, networks, communicationprotocols, and/or other components required for communicating withexternal data stores and/or remote servers. Consequently, at least oneembodiment of DAM described herein can assist with reducing oreliminating the additional computational resources (e.g., memory,processing power, computational time, etc.) that may be associated withusing databases for DAM.

FIG. 1A illustrates, in block diagram form, a processing system 100 thatincludes electronic components for performing digital asset management(DAM) in accordance with this disclosure. The system 100 can be housedin single computing system, such as a desktop computer system, a laptopcomputer system, a tablet computer system, a server computer system, amobile phone, a media player, a personal digital assistant (PDA), apersonal communicator, a gaming device, a network router or hub, awireless access point (AP) or repeater, a set-top box, or a combinationthereof. Components in the system 100 can be spatially separated andimplemented on separate computing systems that are connected by thecommunication technology 110, as described in further detail below.

For one embodiment, the system 100 may include processing unit(s) 130,memory 160, a DA capture device 120, sensor(s) 191, and peripheral(s)190. For one embodiment, one or more components in the system 100 may beimplemented as one or more integrated circuits (ICs). For example, atleast one of the processing unit(s) 130, the communication technology110, the DA capture device 120, the peripheral(s) 190, the sensor(s)191, or the memory 160 can be implemented as a system-on-a-chip (SoC)IC, a three-dimensional (3D) IC, any other known IC, or any known ICcombination. For another embodiment, two or more components in thesystem 100 are implemented together as one or more ICs. For example, atleast two of the processing unit(s) 130, the communication technology110, the DA capture device 120, the peripheral(s) 190, the sensor(s)191, or the memory 160 are implemented together as an SoC IC. Eachcomponent of system 100 is described below.

As shown in FIG. 1A, the system 100 can include processing unit(s) 130,such as CPUs, GPUs, other integrated circuits (ICs), memory, and/orother electronic circuitry. For one embodiment, the processing unit(s)130 manipulate and/or process metadata 170 or optional data 180associated with digital assets (e.g., manipulate computer graphics,perform image processing, manipulate audio files, any other knownprocessing operations performed on DAs, etc.). The processing unit(s)130 may include a digital asset management (DAM) module/logic 140 forperforming one or more embodiments of DAM, as described herein. For oneembodiment, the DAM module/logic 140 is implemented as hardware (e.g.,electronic circuitry associated with the processing unit(s) 130,circuitry, dedicated logic, etc.), software (e.g., one or moreinstructions associated with a computer program executed by theprocessing unit(s) 130, software run on a general-purpose computersystem or a dedicated machine, etc.), or a combination thereof.

The DAM module/logic 140 can enable the system 100 to generate and use aknowledge graph metadata network (metadata network) 175 of the DAmetadata 170 as a multidimensional network. Metadata networks andmultidimensional networks are described below. FIG. 1B (which isdescribed below) provides additional details about generating themetadata network 175. For one embodiment, the DAM module/logic 140 canperform one or more of the following: (i) generate the metadata network175; (ii) relate and/or present at least two DAs based on the metadatanetwork 175; (iii) determine and/or present interesting DAs in the DAcollection based on the metadata network 175 and predeterminedcriterion; and (iv) select and/or present representative DAs tosummarize a moment's DAs based on input specifying the representativegroup's size. Additional details about the immediately precedingoperations performed by the DAM logic/module 140 are described below inconnection with FIGS. 1B-6.

The DAM module/logic 140 can obtain or receive a collection of DAmetadata 170 associated with a DA collection. As used herein, a “digitalasset,” a “DA,” and their variations refer to data that can be stored inor as a digital form (e.g., a digital file etc.). This digitalized dataincludes, but is not limited to, the following: image media (e.g., astill or animated image, etc.); audio media (e.g., a song, etc.); textmedia (e.g., an E-book, etc.); video media (e.g., a movie, etc.); andhaptic media (e.g., vibrations or motions provided in connection withother media, etc.). The examples of digitalized data above can becombined to form multimedia (e.g., a computer animated cartoon, a videogame, etc.). A single DA refers to a single instance of digitalized data(e.g., an image, a song, a movie, etc.). Multiple DAs or a group of DAsrefers to multiple instances of digitalized data (e.g., multiple images,multiple songs, multiple movies, etc.). Throughout this disclosure, theuse of “a DA” refers to “one or more DAs” including a single DA and agroup of DAs. For brevity, the concepts set forth in this document usean operative example of a DA as one or more images. It is to beappreciated that a DA is not so limited and the concepts set forth inthis document are applicable to other DAs (e.g., the different mediadescribed above, etc.).

As used herein, a “digital asset collection,” a “DA collection,” andtheir variations refer to multiple DAs that may be stored in one or morestorage locations. The one or more storage locations may be spatially orlogically separated as is known.

As used herein, “metadata,” “digital asset metadata,” “DA metadata,” andtheir variations collectively refer to information about one or moreDAs. Metadata can be: (i) a single instance of information aboutdigitalized data (e.g., a time stamp associated with one or more images,etc.); or (ii) a grouping of metadata, which refers to a group comprisedof multiple instances of information about digitalized data (e.g.,several time stamps associated with one or more images, etc.). There aredifferent types of metadata. Each type of metadata (also referred to as“metadata type”) describes one or more characteristics or attributesassociated with one or more DAs. Each metadata type can be categorizedas primitive metadata or inferred metadata, as described in furtherdetail below.

For one embodiment, the DAM module/logic 140 can identify primitivemetadata associated with one or more DAs within the DA metadata 170. Fora further embodiment, the DAM module/logic 140 may determine inferredmetadata based at least on the primitive metadata.

As used herein, “primitive metadata” refers to metadata that describesone or more characteristics or attributes associated with one or moreDAs. That is, primitive metadata includes acquired metadata describingone or more DAs. In some scenarios, primitive metadata can be extractedfrom inferred metadata, as described in further detail below. Inaccordance with this disclosure, there are two categories of primitivemetadata—(i) primary primitive metadata; and (ii) auxiliary primitivemetadata.

Primary primitive metadata can include one or more of: time metadata;geo-position metadata; geolocation metadata; people metadata; scenemetadata; content metadata; object metadata; and sound metadata. Timemetadata refers to a time associated with one or more DAs (e.g., atimestamp associated with a DA, a time the DA is generated, a time theDA is modified, a time the DA is stored, a time the DA is transmitted, atime the DA is received, etc.). Geo-position metadata refers togeographic or spatial attributes associated with one or more DAs using ageographic coordinate system (e.g., latitude, longitude, and/oraltitude, etc.). Geolocation metadata refers to one or more meaningfullocations associated with one or more DAs rather than geographiccoordinates associated with the DA(s). Examples include a beach (and itsname,) a street address, a country name, a region, a building, alandmark, etc. Geolocation metadata can, for example, be determined byprocessing geo-position information together with data from a mapapplication to determine that the geolocation for a scene in a group ofimages. People metadata refers to at least one detected or known personassociated with one or more DAs (e.g., a known person in an imagedetected through facial recognition techniques, etc.). Scene metadatarefers to an overall description of an activity or situation associatedwith one or more DAs. For example, if a DA includes a group of images,then scene metadata for the group of images can be determined usingdetected objects in images. For a more specific example, the presence ofa large cake with candles and balloons in at least two images in thegroup can be used to determine that the scene for the group of images isa birthday celebration. Object metadata refers to one or more detectedobjects associated with one or more DAs (e.g., a detected animal, adetected company logo, a detected piece of furniture, etc.). Contentmetadata refers to the features of a DA (e.g., pixel characteristics,pixel intensity values, luminance values, brightness values, loudnesslevels, etc., etc.). Sound metadata refers to one or more detectedsounds associated with one or more DAs (e.g., a detected sound is ahuman's voice, a detected sound is a fire truck's siren, etc.).

Auxiliary primitive metadata includes, but is not limited to, thefollowing: (i) a condition associated with capturing one or more DAs;(ii) a condition associated with modifying one or more DAs; and (iii) acondition associated with storing or retrieving one or more DAs.Examples of a condition associated with capturing a DA include, but arenot limited to, an image sensor or other electronic component used togenerate a DA. Examples of a condition associated with modifying a DAinclude, but are not limited to an algorithm or operation performed on aDA to convert it from one format to another, and an algorithm oroperation performed on a DA to edit the DA's characteristics. Examplesof a condition associated with storing or retrieving a DA include, butare not limited to, a memory cell's logical address, a storage element'slogical address, a network host at which the DA resides, and a physicaladdress represented as a binary number on the address bus circuitry inorder to enable a data bus to access a particular storage cell or aregister in a memory mapped I/O device.

For an illustrative example, primitive metadata associated with a DA(e.g., one or more images, etc.) can include the following: a capturetime associated with the one or more images; a modification timeassociated with the one or more images; a storage time associated withthe one or more images; a storage location associated with the one ormore images; an image processing operation performed on the one or moreimages; pixel values describing pixel intensities in the one or moreimages; a category/name of an imaging sensor used to capture the one ormore images; and a geographic or spatial location (e.g., latitude,longitude, altitude, etc.) associated with capture, modification,storage, or processing of the one or more images as obtained from aglobal positioning system (GPS) or other known tracking device.

As used herein, “inferred metadata” refers to additional informationabout one or more DAs that is beyond the information provided byprimitive metadata. One difference between primitive metadata andinferred metadata is that primitive metadata represents an initial setof descriptions of one or more DA while inferred metadata providesadditional descriptions of the one or more DAs based on processing oneor more of the primitive metadata (i.e., the initial set ofdescriptions) and contextual information. For example, primitivemetadata may identify two detected persons in a group of images as JohnDoe and Jane Doe, while inferred metadata may identify John Doe and JaneDoe as a married couple based on processing one or more of the primitivemetadata (i.e., the initial set of descriptions) and contextualinformation. For one embodiment, inferred metadata is formed from atleast one of: (i) a combination of different types of primitivemetadata; (ii) a combination of different types of contextualinformation; or (iii) a combination of primitive metadata and contextualinformation.

As used herein, “context” and its variations refer to any or allattributes of a user's device that includes or has access to a DAcollection associated with the user, such as physical, logical, social,and other contextual information. As used herein, “contextualinformation” and its variations refer to metadata that describes ordefines a user's context or a context of a user's device that includesor has access to a DA collection associated with the user. Exemplarycontextual information includes, but is not limited to, the following: apredetermined time interval; an event scheduled to occur in apredetermined time interval; a geolocation to be visited in apredetermined time interval; one or more identified persons associatedwith a predetermined time; an event scheduled for a predetermined time,or a geolocation to be visited at predetermined time; weather metadatadescribing weather associated with a particular period in time (e.g.,rain, snow, sun, temperature, etc.); season metadata describing a seasonassociated with capture of the image. For some embodiments, thecontextual information can be obtained from external sources, a socialnetworking application, a weather application, a calendar application,an address book application, any other type of application, or from anytype of data store accessible via a wired or wireless network (e.g., theInternet, a private intranet, etc.).

Two categories of inferred metadata are set forth herein—(i) primaryinferred metadata; and (ii) auxiliary inferred metadata. Primaryinferred metadata can include event metadata describing one or moreevents associated with one or more DAs. For example, if a DA includesone or more images, the primary inferred metadata can include eventmetadata describing one or more events where the one or more images werecaptured (e.g., a vacation, a birthday, a sporting event, a concert, agraduation ceremony, a dinner, a project, a work-out session, atraditional holiday, etc.). Primary inferred metadata can, in someembodiments, be determined by clustering one or more of primaryprimitive metadata, auxiliary primitive metadata, and contextualmetadata.

Auxiliary inferred metadata includes, but is not limited to, thefollowing: (i) geolocation relationship metadata; (iii) personrelationship metadata; (iii) object relationship metadata; and (iv)sound relationship metadata. Geolocation relationship metadata refers toa relationship between one or more known persons associated with one ormore DAs and one or more meaningful locations associated with the one ormore DAs. For example, an analytics engine or data mining technique canbe used to determine that a scene associated with one or more images ofJohn Doe represents John Doe's home. Person relationship metadata refersto a relationship between one or more known persons associated with oneor more DAs and one or more other known persons associated with the oneor more DAs. For example, an analytics engine or data mining techniquecan be used to determine that Jane Doe (who appears in one or moreimages with John Doe) is John Doe's wife. Object relationship metadatarefers to a relationship between one or more known objects associatedwith one or more DAs and one or more known persons associated with theone or more DAs. For example, an analytics engine or data miningtechnique can be used to determine that a boat appearing in one or moreimages with John Doe is owned by John Doe. Sound relationship metadatarefers to a relationship between one or more known sounds associatedwith one or more DAs and one or more known persons associated with theone or more DAs. For example, an analytics engine or data miningtechnique can be used to determine that a voice that appears in one ormore videos with John Doe is John Doe's voice.

As explained above, inferred metadata may be determined or inferred fromprimitive metadata and/or contextual information by performing at leastone of the following: (i) data mining the primitive metadata and/orcontextual information; (ii) analyzing the primitive metadata and/orcontextual information; (iii) applying logical rules to the primitivemetadata and/or contextual information; or (iv) any other known methodsused to infer new information from provided or acquired information.Also, primitive metadata can be extracted from inferred metadata. For aspecific embodiment, primary primitive metadata (e.g., time metadata,geolocation metadata, scene metadata, etc.) can be extracted fromprimary inferred metadata (e.g., event metadata, etc.). Techniques fordetermining inferred metadata and/or extracting primitive metadata frominferred metadata can be iterative. For a first example, inferringmetadata can trigger the inference of other metadata and so on. For asecond example, extracting primitive metadata from inferred metadata cantrigger inference of additional inferred metadata or extraction ofadditional primitive metadata.

Referring again to FIG. 1A, the primitive metadata and the inferredmetadata described above are collectively referred to as the DA metadata170. For one embodiment, the DAM module/logic 140 uses the DA metadata170 to generate a metadata network 175. As shown in FIG. 1A, all or someof the metadata network 175 can be stored in the processing unit(s) 130and/or the memory 160. As used herein, a “knowledge graph,” a “knowledgegraph metadata network,” a “metadata network,” and their variationsrefer to a dynamically organized collection of metadata describing oneor more DAs (e.g., one or more groups of DAs in a DA collection, one ormore DAs in a DA collection, etc.) used by one or more computer systemsfor deductive reasoning. In a metadata network, there is no DA—onlymetadata (e.g., metadata associated with one or more groups of DAs,metadata associated with one or more DAs, etc.). Metadata networksdiffer from databases because, in general, a metadata network enablesdeep connections between metadata using multiple dimensions, which canbe traversed for additionally deduced correlations. This deductivereasoning generally is not feasible in a conventional relationaldatabase without loading a significant number of database tables (e.g.,hundreds, thousands, etc.). As such, conventional databases may requirea large amount of computational resources (e.g., external data stores,remote servers, and their associated communication technologies, etc.)to perform deductive reasoning. In contrast, a metadata network may beviewed, operated, and/or stored using fewer computational resourcerequirements than the preceding example of databases. Furthermore,metadata networks are dynamic resources that have the capacity to learn,grow, and adapt as new information is added to them. This is unlikedatabases, which are useful for accessing cross-referred information.While a database can be expanded with additional information, thedatabase remains an instrument for accessing the cross-referredinformation that was put into it. Metadata networks do more than accesscross-referred information—they go beyond that and involve theextrapolation of data for inferring or determining additional data.

As explained in the preceding paragraph, a metadata network enables deepconnections between metadata using multiple dimensions in the metadatanetwork, which can be traversed for additionally deduced correlations.Each dimension in the metadata network may be viewed as a grouping ofmetadata based on metadata type. For example, a grouping of metadatacould be all time metadata assets in a metadata collection and anothergrouping could be all geo-position metadata assets in the same metadatacollection. Thus, for this example, a time dimension refers to all timemetadata assets in the metadata collection and a geo-position dimensionrefers to all geo-position metadata assets in the same metadatacollection. Furthermore, the number of dimensions can vary based onconstraints. Constraints include, but are not limited to, a desired usefor the metadata network, a desired level of detail, and/or theavailable metadata or computational resources used to implement themetadata network. For example, the metadata network can include only atime dimension, the metadata network can include all types of primitivemetadata dimensions, etc. With regard to the desired level of detail,each dimension can be further refined based on specificity of themetadata. That is, each dimension in the metadata network is a groupingof metadata based on metadata type and the granularity of informationdescribed by the metadata. For a first example, there can be two timedimensions in the metadata network, where a first time dimensionincludes all time metadata assets classified by week and the second timedimension includes all time metadata assets classified by month. For asecond example, there can be two geolocation dimensions in the metadatanetwork, where a first geolocation dimension includes all geolocationmetadata assets classified by type of establishment (e.g., home,business, etc.) and the second geolocation dimension includes allgeolocation metadata assets classified by country. The precedingexamples are merely illustrative and not restrictive. It is to beappreciated that the level of detail for dimensions can vary dependingon designer choice, application, available metadata, and/or availablecomputational resources.

The DAM module/logic 140 may generate the metadata network 175 as amultidimensional network of the DA metadata 170. As used herein, a“multidimensional network” and its variations refer to a complex graphhaving multiple kinds of relationships. A multidimensional networkgenerally includes multiple nodes and edges. For one embodiment, thenodes represent metadata, and the edges represent relationships orcorrelations between the metadata. Exemplary multidimensional networksinclude, but are not limited to, edge-labeled multigraphs, multipartiteedge-labeled multigraphs, and multilayer networks.

For one embodiment, the nodes in the metadata network 175 representmetadata assets found in the DA metadata 170. For example, each noderepresents a metadata asset associated with one or more DAs in a DAcollection. For another example, each node represents a metadata assetassociated with a group of DAs in a DA collection. As used herein, a“metadata asset” and its variations refer to metadata (e.g., a singleinstance of metadata, a group of multiple instances of metadata, etc.)describing one or more characteristics of one or more DAs in a DAcollection. As such, there can be a primitive metadata asset, aninferred metadata asset, a primary primitive metadata asset, anauxiliary primitive metadata asset, a primary inferred metadata asset,and/or an auxiliary inferred metadata asset. For a first example, aprimitive metadata asset refers to a time metadata asset describing atime interval between Jun. 1, 2016 and Jun. 3, 2016 when one or more DAswere captured. For a second example, a primitive metadata asset refersto a geo-position metadata asset describing one or more latitudes and/orlongitudes where one or more DAs were captured. For a third example, aninferred metadata asset refers to an event metadata asset describing avacation in Paris, France between Jun. 5, 2016 and Jun. 30, 2016 whenone or more DAs were captured.

For one embodiment, the metadata network 175 includes two types ofnodes—(i) moment nodes; and (ii) non-moments nodes. As used herein, a“moment” refers a single event (as described by an event metadata asset)that is associated with one or more DAs. For example, a moment refers toa vacation in Paris, France that lasted between Jun. 1, 2016 and Jun. 9,2016. For this example, the moment can be used to identify one or moreDAs (e.g., one image, a group of images, a video, a group of videos, asong, a group of songs, etc.) associated with the vacation in Paris,France that lasted between Jun. 1, 2016 and Jun. 9, 2016 (and not withany other event).

As used herein, a “moment node” refers to a node in a multidimensionalnetwork that represents a moment (which is described above). Thus, amoment node refers to a primary inferred metadata asset representing asingle event associated with one or more DAs. Primary inferred metadatais described above. As used herein, a “non-moment node” refers a node ina multidimensional network that does not represent a moment. Thus, anon-moment node refers to at least one of the following: (i) a primitivemetadata asset associated with one or more DAs; or (ii) an inferredmetadata asset associated with one or more DAs that is not a moment(i.e., not an event metadata asset).

As used herein, an “event” and its variations refer to a situation or anactivity occurring at one or more locations during a specific timeinterval. An event includes, but is not limited to the following: agathering of one or more persons to perform an activity (e.g., aholiday, a vacation, a birthday, a dinner, a project, a work-outsession, etc.); a sporting event (e.g., an athletic competition, etc.);a ceremony (e.g., a ritual of cultural significance that is performed ona special occasion, etc.); a meeting (e.g., a gathering of individualsengaged in some common interest, etc.); a festival (e.g., a gathering tocelebrate some aspect in a community, etc.); a concert (e.g., anartistic performance, etc.); a media event (e.g., an event created forpublicity, etc.); and a party (e.g., a large social or recreationalgathering, etc.).

For one embodiment, the edges in the metadata network 175 between nodesrepresent relationships or correlations between the nodes. For oneembodiment, the DAM module/logic 140 updates the metadata network 175 asthe DAM module/logic 140 obtains or receives new primitive metadata 170and/or determines new inferred metadata 170 based on the new primitivemetadata 170.

The DAM module/logic 140 can manage DAs associated with the DA metadata170 using the metadata network 175. For a first example, DAMmodule/logic 140 can use the metadata network 175 to relate multiple DAsbased on the correlations (i.e., the edges in the metadata network 175)between the DA metadata 170 (i.e., the nodes in the metadata network175). For this first example, the DAM module/logic 140 relates the afirst group of one or more DAs with a second group of one or more DAsbased on the metadata assets that are represented as moment nodes in themetadata network 175. For a second example, DAM module/logic 140 usesthe metadata network 175 to locate and present interesting groups of oneor more DAs in DA collection based on the correlations (i.e., the edgesin the metadata network 175) between the DA metadata (i.e., the nodes inthe metadata network 175) and predetermined criterion. For this secondexample, the DAM module/logic 140 selects the interesting DAs based onmoment nodes in the metadata network 175. Furthermore, and for thissecond example, the predetermined criterion refers to contextualinformation (which is described above). The predetermined time intervalcan be a current time interval or a future time interval. For a thirdexample, the DAM module/logic 140 uses the metadata network 175 toselect and present a representative group of one or more DAs thatsummarize a moment's DAs based on the correlations (i.e., the edges inthe metadata network 175) between the DA metadata (i.e., the nodes inthe metadata network 175) and input specifying the representativegroup's size. For this third example, the DAM module/logic 140 selectsthe representative DAs based on an event metadata asset. The eventmetadata asset can, but is not required to, be a moment node in themetadata network 175 associated with one or more DAs.

The system 100 can also include memory 160 for storing and/or retrievingmetadata 170, the metadata network 175, and/or optional data 180described by or associated with the metadata 170. The metadata 170, themetadata network 175, and/or the optional data 180 can be generated,processed, and/or captured by the other components in the system 100.For example, the metadata 170, the metadata network 175, and/or theoptional data 180 includes data generated by, captured by, processed by,or associated with one or more peripherals 190, the DA capture device120, or the processing unit(s) 130, etc. The system 100 can also includea memory controller (not shown), which includes at least one electroniccircuit that manages data flowing to and/or from the memory 160. Thememory controller can be a separate processing unit or integrated inprocessing unit(s) 130.

The system 100 can include a DA capture device 120 (e.g., an imagingdevice for capturing images, an audio device for capturing sounds, amultimedia device for capturing audio and video, any other known DAcapture device, etc.). Device 120 is illustrated with a dashed box toshow that it is an optional component of the system 100. Nevertheless,the DA capture device 120 is not always an optional component of thesystem 100—some embodiments of the system 100 may require the DA capturedevice 120 (e.g., a camera, a smartphone with a camera, etc.). For oneembodiment, the DA capture device 120 can also include a signalprocessing pipeline that is implemented as hardware, software, or acombination thereof. The signal processing pipeline can perform one ormore operations on data received from one or more components in thedevice 120. The signal processing pipeline can also provide processeddata to the memory 160, the peripheral(s) 190, and/or the processingunit(s) 130.

The system 100 can also include peripheral(s) 190. For one embodiment,the peripheral(s) 190 can include at least one of the following: (i) oneor more input devices that interact with or send data to one or morecomponents in the system 100 (e.g., mouse, keyboards, etc.); (ii) one ormore output devices that provide output from one or more components inthe system 100 (e.g., monitors, printers, display devices, etc.); or(iii) one or more storage devices that store data in addition to thememory 160. Peripheral(s) 190 is illustrated with a dashed box to showthat it is an optional component of the system 100. Nevertheless, theperipheral(s) 190 is not always an optional component of the system100—some embodiments of the system 100 may require the peripheral(s) 190(e.g., a smartphone with media recording and playback capabilities,etc.). The peripheral(s) 190 may also refer to a single component ordevice that can be used both as an input and output device (e.g., atouch screen, etc.). The system 100 may include at least one peripheralcontrol circuit (not shown) for the peripheral(s) 190. The peripheralcontrol circuit can be a controller (e.g., a chip, an expansion card, ora stand-alone device, etc.) that interfaces with and is used to directoperation(s) performed by the peripheral(s) 190. The peripheral(s)controller can be a separate processing unit or integrated in processingunit(s) 130. The peripheral(s) 190 can also be referred to asinput/output (I/O) devices 190 throughout this document.

The system 100 can also include one or more sensors 191, which areillustrated with a dashed box to show that the sensor can be optionalcomponents of the system 100. Nevertheless, the sensor(s) 191 are notalways optional components of the system 100—some embodiments of thesystem 100 may require the sensor(s) 191 (e.g., a camera that includesan imaging sensor, etc.). For one embodiment, the sensor(s) 191 candetect a characteristic of one or more environs. Examples of a sensorinclude, but are not limited to, a light sensor, an imaging sensor, anaccelerometer, a sound sensor, a barometric sensor, a proximity sensor,a vibration sensor, a gyroscopic sensor, a compass, a barometer, a heatsensor, a rotation sensor, a velocity sensor, and an inclinometer.

For one embodiment, the system 100 includes communication mechanism 110.The communication mechanism 110 can be a bus, a network, or a switch.When the technology 110 is a bus, the technology 110 is a communicationsystem that transfers data between components in system 100, or betweencomponents in system 100 and other components associated with othersystems (not shown). As a bus, the technology 110 includes all relatedhardware components (wire, optical fiber, etc.) and/or software,including communication protocols. For one embodiment, the technology110 can include an internal bus and/or an external bus. Moreover, thetechnology 110 can include a control bus, an address bus, and/or a databus for communications associated with the system 100. For oneembodiment, the technology 110 can be a network or a switch. As anetwork, the technology 110 may be any network such as a local areanetwork (LAN), a wide area network (WAN) such as the Internet, a fibernetwork, a storage network, or a combination thereof, wired or wireless.When the technology 110 is a network, the components in the system 100do not have to be physically co-located. When the technology 110 is aswitch (e.g., a “cross-bar” switch), separate components in system 100may be linked directly over a network even though these components maynot be physically located next to each other. For example, two or moreof the processing unit(s) 130, the communication technology 110, thememory 160, the peripheral(s) 190, the sensor(s) 191, and the DA capturedevice 120 are in distinct physical locations from each other and arecommunicatively coupled via the communication technology 110, which is anetwork or a switch that directly links these components over a network.

FIG. 1B illustrates, in block diagram form, an exemplary metadatanetwork 175 in accordance with one embodiment. The exemplary metadatanetwork 175 illustrated in FIG. 1B can be generated and used by theprocessing system 100 illustrated in FIG. 1A to perform DAM inaccordance with an embodiment. For one embodiment, the metadata network175 illustrated in FIG. 1B is similar to or the same as the metadatanetwork 175 described above in connection with FIG. 1A. It is to beappreciated that the metadata network 175 described in FIG. 1B isexemplary and that every node that can be generated by the DAMmodule/logic 140 is not shown. For example, even though every possiblenode is not illustrated in FIG. 1B, the DAM module/logic 140 cangenerate a node to represent each metadata asset illustrated in boxes205-210 of FIG. 1B.

In the metadata network 175 illustrated in FIG. 1B, nodes representingmetadata are illustrated as circles and edges representing correlationsbetween the metadata are illustrated as labeled connections betweencircles. Furthermore, moment nodes are represented as circles withthickened boundaries while other non-moment nodes lack the thickenedboundaries. In addition, the metadata assets shown in boxes 205, 210,and 215 can be represented as non-moment nodes in the metadata network175.

Generating the metadata network 175, by the DAM module/logic 140, caninclude defining nodes based on the primitive metadata and/or theinferred metadata associated with one or more DAs in a DA collection. Asthe DAM module/logic 140 identifies more primitive metadata within themetadata associated with a DA collection and/or infers metadata from atleast the primitive metadata, the DAM module/logic 140 can generateadditional nodes to represent the primitive metadata and/or the inferredmetadata. Furthermore, as the DAM module/logic 140 determinescorrelations between nodes, the DAM module/logic 140 can create edgesbetween the nodes. Two generation processes can be used to create themetadata network 175. The first generation process is initiated using ametadata asset that does not describe a moment (e.g., primary primitivemetadata asset, an auxiliary primitive metadata asset, an auxiliaryinferred metadata asset etc.). The second generation process isinitiated using a metadata asset that describes a moment (e.g., an eventmetadata). Each of these generation processes is described below.

For the first generation process, the DAM module/logic 140 can generatea non-moment node 223 to represent metadata associated with a user, aconsumer, or an owner of a DA collection associated with the metadatanetwork 175. As illustrated in FIG. 1B, a user is identified as JeanDupont. For one embodiment, the DAM module/logic 140 generates thenon-moment node 223 to represent the metadata 210 provided by the user(e.g., Jean Dupont, etc.) via an input device. For example, the user canadd at least some of the metadata 210 about herself or himself to themetadata network 175 via an input device. In this way, the DAMmodule/logic 140 can use the metadata 210 to correlate the user withother metadata acquired from a DA collection. For example, and as shownin FIG. 1B, the metadata 210 provided by the user Jean Dupont caninclude one or more of his name, his birthplace (which is Paris,France), his birthdate (which is May 27, 1991), his gender (which ismale), his relationship status (which is married), his significant otheror spouse (which is Marie Dupont), and his current residence (which isin Key West, Fla., USA).

Still with regard to the first generation process, at least some of themetadata 210 can be predicted based on processing performed by the DAMmodule/logic 140. The DAM module/logic 140 may predict metadata 210based on an analysis of metadata accessed via an application or metadatain a data store (e.g., memory 160 of FIG. 1, etc.). For example, the DAMmodule/logic 140 may predict the metadata 210 based on analyzinginformation acquired by accessing the user's contacts (via a contactsapplication), activities (via a calendar application or an organizationapplication), contextual information (via sensor(s) 191 and/orperipheral(s) 190), and/or social networking data (via a socialnetworking application).

For one embodiment, the metadata 210 includes, but is not limited to,other metadata, such as the user's relationships with other others(e.g., family members, friends, co-workers, etc.), the user's workplaces(e.g., past workplaces, present workplaces, etc.), the user's interests(e.g., hobbies, DAs owned, DAs consumed, DAs used, etc.), places visitedby the user (e.g., previous places visited by the user, places that willbe visited by the user, etc.). For one embodiment, the metadata 210 canbe used alone or in conjuction with other data to determine or infer atleast one of the following: (i) vacations or trips taken by Jean Dupont(e.g., nodes 231, etc.); days of the week (e.g., weekends, holidays,etc.); locations associated with Jean Dupont (e.g., nodes 231, 233, 235,etc.); Jean Dupont's social group (e.g., his wife Marie Dupontrepresented in node 227, etc.); Jean Dupont's professional or othergroups (e.g., groups based on his occupation, etc.); types of placesvisited by Jean Dupont (e.g., Prime 114 restaurant represented in node229, Home represented by node 225, etc.); activities performed (e.g., awork-out session, etc.); etc. The preceding examples are illustrativeand not restrictive.

For the second generation process in FIG. 1B, the metadata network 175may include at least one moment node—for example, the moment node 220Aand moment node 220B. Other embodiments of the metadata network 175,however, are not so limited. For example, the metadata network 175 caninclude less than two moment nodes or more than two moment nodes. Forthis second generation process, the DAM module/logic 140 generates themoment node 220A and the moment node 220B to represent one or moreprimary inferred metadata assets (e.g., an event metadata asset, etc.).The DAM module/logic 140 can determine or infer the primary inferredmetadata (e.g., an event metadata asset, etc.) from one or more of theinformation 210, the metadata 205, the metadata 215, and other datareceived from external sources (e.g., weather application, calendarapplication, social networking application, address book application,etc.). Also, the DAM module/logic 140 may receive the primary inferredmetadata assets, generate this metadata as the moment node 220A and themoment node 220B, and extract primary primitive metadata 205 and 215from the primary inferred metadata assets represented as the moment node220A and the moment node 220B. The primary primitive metadata assetsillustrated in boxes 205 and 215 can include more or less than themetadata assets illustrated in FIG. 1B. For example, primary primitivemetadata can also include altitude, relative geographical coordinates,week of the year, day of the week, month of the year, season, relativetime, additional objects, additional scene descriptions, etc.

For one embodiment, the metadata network 175 also includes non-momentnodes 223, 225, 227, 229, 231, 233, 235, and 237. The DAM module/logic140 can generate additional nodes based on moment nodes as follows: (i)the DAM module/logic 140 determines auxiliary primitive metadata assetsassociated with the moment nodes 220A-B by cross-referencing theauxiliary primitive metadata assets with primary primitive metadataassets and/or primary inferred metadata assets in a metadata collection;(ii) the DAM module/logic 140 determines or infers auxiliary inferredmetadata assets associated with the moment nodes 220A-B based on theauxiliary primitive metadata assets, the primary primitive metadataassets, and/or the primary inferred metadata assets; and (iii) the DAMmodule/logic 140 generates a node for each auxiliary inferred metadataasset, each auxiliary primitive metadata asset, each primary primitivemetadata asset, and/or each primary inferred metadata asset. For a firstexample, and as illustrated in FIG. 1B, the DAM module/logic 140generates non-moment nodes 233, 231, 229, 235, and 237 after determiningand/or inferring metadata assets associated with the moment node 220A.For a second example, the DAM module/logic 140 generates nodes 225 and227 after determining and/or inferring metadata assets associated withthe moment node 220B.

For one embodiment, the DAM module/logic 140 can refine each metadataasset associated with the moment nodes 220A-B based on a probabilitydistribution (e.g., a discrete probability distribution, a continuousprobability distribution, etc.). For example, a Gaussian distributionmay be used to determine a distribution of the primary primitivemetadata assets. For this example, the distribution may be used toascertain a mean, a median, a mode, a standard deviation, and/or avariance associated with the distribution of the primary primitivemetadata assets. The DAM module/logic 140 can use the Gaussiandistribution to select or filter out a sub-set of the primary primitivemetadata assets that is within a predetermined criterion (e.g., 1standard deviation (68%), 2 standard deviations (95%), or 3 standarddeviations (99.7%), etc.). Hence, this selection/filtering operation canassist with identifying relevant primary primitive metadata assets forDAM and with filtering out noise or unreliable primary primitivemetadata assets. Consequently, all the other types of metadata (e.g.,auxiliary primitive metadata assets, primary inferred metadata assets,auxiliary inferred metadata assets, etc.) that are associated with,determined from, or inferred from the primary primitive metadata assetsmay also be relevant and relatively noise-free. For a second example, aGaussian distribution may be used to determine a distribution of theprimary inferred metadata assets (i.e., moment nodes). For this example,the distribution may be used to ascertain a mean, a median, a mode, astandard deviation, and/or a variance associated with the distributionof the moments. The DAM module/logic 140 can use the Gaussiandistribution to select or filter out a sub-set of the primary inferredmetadata assets (i.e., moment nodes) that is within a predeterminedcriterion (e.g., 1 standard deviation (68%), 2 standard deviations(95%), or 3 standard deviations (99.7%), etc.). Hence, thisselection/filtering operation can assist with identifying relevantprimary inferred metadata assets (i.e., moment nodes) for DAM and withfiltering out noise or unreliable primary inferred metadata assets.Consequently, all the other types of metadata (e.g., primary primitivemetadata assets, auxiliary primitive metadata assets, auxiliary inferredmetadata assets, etc.) that are associated with, determined from, orextracted from the primary inferred metadata assets may also be relevantand relatively noise-free.

Noise can occur due to primary primitive metadata assets that areassociated one or more irrelevant DAs. Such DAs can be determined basedon the number of DAs associated with a primary primitive metadata asset.For example, a primary primitive metadata asset associated with two orless DAs can be designated as noise. This is because such metadataassets (and their DAs) may be irrelevant given the little informationthey provide. For example, the more important or significant an event isto a user, the higher the likelihood that the event is captured using alarge number of images (e.g., three or more, etc.). For this example,the probability distribution described above can enable selecting theprimary primitive metadata asset associated with these DAs. This isbecause the number of DAs associated with the event may suggest animportance or relevance of the primary primitive metadata asset. Incontrast, insignificant events may have only one or two images, and thecorresponding primary primitive metadata asset may not add much to DAMbased on the metadata network described herein. The immediatelypreceding examples are also applicable to the primary inferred metadata,the auxiliary primitive metadata, and the auxiliary inferred metadata.

For one embodiment, the DAM module/logic 140 determines a confidenceweight and/or a relevance weight for at least some, and possibly each,of the primary primitive metadata assets, the primary inferred metadataassets, the auxiliary primitive metadata assets, and the auxiliaryinferred metadata assets associated with the moment node 220A-B.

As used herein, a “confidence weight” and its variations refer to avalue (e.g., an integer, etc.) used to describe a certainty that somemetadata correctly identifies a feature or characteristic of one or moreDAs associated with a moment. For example, a confidence weight of 0.6(out of a maximum of 1.0) can be used to indicate a 60% confidence levelthat a feature in one or more digital images associated with a moment isa dog.

As used herein, a “relevance weight” and its variations refer to a value(e.g., an integer, etc.) used to describe an importance assigned to afeature or characteristic of one or more DAs associated with a moment asidentified by a metadata asset. For example, a first relevance weight of0.85 (out of a maximum of 1.0) can be used indicate that a firstidentified feature in a digital image (e.g., a person) is very importantwhile a second relevance weight of 0.50 (out of a maximum of 1.0) can beused indicate that a second identified feature in a digital image (e.g.,a dog) is not very important.

As shown in FIG. 1B, and for one example, the DAM module/logic 140estimates that one or more metadata assets associated with the momentnode 220A describe Jean Dupont's birthday. For this example, theconfidence weight 239 is assigned a value of 0.8 to indicate an 80%confidence level that Jean Dupont's birthday is described by one or moremetadata assets illustrated in box 205. Furthermore, and for thisexample, a relevance weight 239 is assigned a value of is 0.9 (out of amaximum of 1.0) to indicate that Jean Dupont's birthday is an importantfeature in the metadata asset(s) illustrated in box 205. For thisexample, the important metadata asset illustrated in box 205 can includethe date associated with moment 220A, which is illustrated as May 27,2016. The DAM module/logic 140 can compare the data shown in box 205with Jean Dupont's known birthday 233 of May 27, 1991 to determine theconfidence weight 235 and the relevance weight 235. For another example,the DAM module/logic 140 may compare Jean Dupont's known birthday 233against some or all metadata assets of a date type until a moment (e.g.,moment 220A) that includes time metadata with the same or similar dateas Jean Dupont's known birthday 233 is found (e.g., the time metadataasset shown in box 205, etc.).

With specific regard to images, confidence weights and relevance weightsmay be detected via feature detection techniques that include analyzingmetadata associated with one or more images. For one embodiment, the DAMmodule/logic 140 can determine confidence levels and relevance weightsusing metadata associated with one or more DAs by applying known featuredetection techniques. Relevance can be statically defined in themetadata network from external constraints. For example, relevance canbe based on information acquired from other sources, like socialnetworking data, calendar data, etc. Also, relevance may be based oninternal constraints. That is, as more detections of a metadata assetare made, its relevance can be increased. Relevance can also retard asfewer detections are made. For example, as more detections of MarieDupont 227 are made over a predetermined period of time (e.g., an hour,a day, a week, a year, etc.), her relevance is increased to indicate herimportance to Jean Dupont. Confidence can be dynamically generated basedon the ingest of metadata in the metadata network. For instance, adetected person in an image may be linked with information about thatperson from a contacts application, a calendar application, socialnetworking application, or other application to determine a level ofconfidence that the detected person is correctly identified. For afurther example, the overall description of a scene in the image may belinked with geo-position information acquired from primary inferredmetadata associated with the detected person to determine the level ofconfidence. Other examples are possible. In addition, confidence can bebased on internal constraints. That is, as more detections of a metadataasset are made, its identification confidence is increased. Confidencecan also retard as fewer detections are made.

The DAM module/logic 140 can generate edges representing correlationsbetween nodes (i.e., the metadata assets) in the metadata network 175.For one embodiment, the DAM module/logic 140 determines correlationsbetween the nodes in the metadata network 175 based on the confidenceweights and the relevance weights. For a further embodiment, the DAMmodule/logic 140 determines correlations between nodes in the metadatanetwork 175 based on the confidence weight between two nodes beinggreater than or equal to a confidence threshold and/or the relevanceweight between two nodes being greater than or equal to a relevancethreshold. For one embodiment, the correlation between the two nodes isdetermined based on a combination of the confidence weight and therelevance weight between the two nodes being equal to or greater than athreshold correlation. For example, and as shown in FIG. 1B, the DAMmodule/logic 140 can generate an edge 239 to indicate a correlationbetween the metadata asset represented by a node 233, which describesJean Dupont's birthday and the metadata asset represented by the momentnode 220A. For this example, the DAM module/logic 140 can generate theedge 239 based on the DAM module/logic 140 determining that theconfidence weight associated with the edge 239 is greater than or equalto a confidence threshold and/or that the relevance weight associatedwith the edge 239 is greater than or equal to a relevance threshold.

Referring now to FIG. 2, which is a flowchart representing an operation200 to perform DAM according to an embodiment. Operation 200 can beperformed by a DAM logic/module (e.g., the DAM module/logic 140described above in connection with FIGS. 1A-1B). Operation 200 begins atblock 291, where a metadata network is received or generated. Themetadata network can be similar to or the same as the metadata network175 described above in connection with FIGS. 1A-1B. The metadata networkcan be obtained from memory (e.g., memory 160 described above inconnection with FIG. 1A). Additionally, or alternatively, the metadatanetwork can be generated by processing unit(s) (e.g., the processingunit(s) 130 described above in connection with FIGS. 1A-1B. Block 291can be performed according to one or more descriptions provided above inconnection with FIGS. 1A-1B. Operation 200 proceeds to block 293, wherea first metadata asset (e.g., a moment node, a non-moment node, etc.) isidentified in the multidimensional network representing the metadatanetwork. For one embodiment, the first metadata asset is represented asa moment node. For this embodiment, the first metadata asset representsa first event associated with one or more DAs. At block 295, a secondmetadata asset is identified or detected based at least on the firstmetadata asset. The second metadata asset may be identified or detectedin the metadata network as a second node (e.g., a moment node, anon-moment node, etc.) based on the first node used to represent thefirst metadata asset. For one embodiment, the second metadata asset isrepresented as a second moment node that differs from the first momentnode. This is because the first moment node represents a first eventmetadata asset that describes a first event associated with one or moreDAs while the second moment node represents a second event metadataasset that describes a second event associated with one or more DAs.

For one embodiment, identifying the second metadata asset (e.g., amoment node, etc.) based on the first metadata asset (e.g., a momentnode, etc.) is performed by determining that the first and secondmetadata assets share a primary primitive metadata asset, a primaryinferred metadata asset, an auxiliary primitive metadata asset, and/oran auxiliary inferred metadata asset even though some of their metadatadiffer. For one embodiment, the shared metadata assets between the firstand second metadata assets may be selected based on the confidenceand/or relevance weights between the metadata assets. The sharedmetadata assets between the first and second metadata asset may beselected based on the confidence and/or relevance weights being equal toor greater than a threshold level of confidence and/or relevance.

For one example, a first moment node could represent a first eventmetadata asset associated with multiple images that were taken at apublic park in Houston, Tex. between Jun. 1, 2016 and Jun. 3, 2016. Forthis example, a second moment node that represents a second moment nodeassociated with multiple images could be identified based on the firstmoment node. The second moment node could be identified by determiningone or more other nodes (i.e., other metadata assets) that areassociated with one or more images that were taken at the same publicpark in Houston, Tex. but on different dates (i.e., not between Jun. 1,2016 and Jun. 3, 2016). For a variation of this example, the secondmoment node could be identified based on the first moment node bydetermining one or more other nodes (i.e., other metadata assets)associated with one or more images that were taken at another publicpark in Houston, Tex. but on different dates (i.e., not between Jun. 1,2016 and Jun. 3, 2016). For yet another variation of this example, thesecond moment node could be identified based on the first moment node bydetermining one or more other nodes (i.e., other metadata assets)associated with one or more images that were taken at another publicpark outside Houston, Tex. but on different dates (i.e., not betweenJun. 1, 2016 and Jun. 3, 2016). Operation 200 can proceed to block 297,where at least one DA associated with the first metadata asset or thesecond metadata asset is presented via an output device. For example,one or more images of the identified public park in Houston, Tex. can bepresented on a display device.

FIG. 3A illustrates, in flowchart form, an operation 300 to generate anexemplary metadata network for DAM in accordance with an embodiment.Operation 300 can be performed by a DAM logic/module (e.g., the DAMlogic/module described above in connection with FIGS. 1A-1B, etc.). Eachof blocks 301-305B can be performed in accord with descriptions providedabove in connection with FIGS. 1A-2.

Operation 300 begins at block 301, where DA metadata associated with aDA collection (hereinafter “a metadata collection”) is obtained orreceived. The metadata collection can be received or obtained from amemory (e.g., memory 160 described above in connection with FIG. 1A,etc.). For one embodiment, the metadata collection includes at least oneof the following: (i) one or more primary primitive metadata assetsassociated with one or more DAs in the DA collection; (ii) one or moreauxiliary primitive metadata assets associated with one or more DAs inthe DA collection; or (iii) one or more primary inferred metadata assetsassociated with one or more DAs in the DA collection.

At block 303, the metadata collection is analyzed for primary primitivemetadata assets, auxiliary primitive metadata assets, primary inferredmetadata assets, and auxiliary inferred metadata assets. The analysis atblock 303 can begin by identifying primary primitive metadata asset(s)and/or primary inferred metadata asset(s) in the metadata collection.When the metadata collection includes primary primitive metadataasset(s), such asset(s) can be used to infer at least one primaryinferred metadata asset. Alternatively or additionally, when themetadata collection includes the primary inferred metadata asset(s), atleast one primary metadata asset can be extracted from the primaryinferred metadata asset(s). For one embodiment, the identified primaryprimitive metadata asset(s) and/or the identified primary inferredmetadata asset(s) may be used to determine at least one auxiliaryprimary metadata asset or infer at least one auxiliary inferred metadataasset.

For an embodiment, the auxiliary primitive metadata asset(s) in themetadata collection may be determined by cross-referencing the primaryprimitive metadata asset(s) and/or the primary inferred metadataasset(s) with auxiliary primitive metadata asset(s) in the same metadatacollection. For example, auxiliary primitive metadata asset(s) can bedetermined by cross-referencing the primary primitive metadata asset(s)and/or the primary inferred metadata asset(s) with some or all othermetadata assets in the metadata collection and excluding any metadataasset in the metadata collection that is not an auxiliary primitivemetadata asset until one or more auxiliary primitive metadata assets arefound. For a specific example, a primary primitive metadata asset thatrepresents a time metadata asset in a metadata collection can be used todetermine an auxiliary primitive metadata asset in the same metadatacollection that represents a condition associated with capturing a DA.For this example, the condition can include determining a workingcondition of an image sensor used to capture the DA at the specific timerepresented by the time metadata asset, which is determined bycross-referencing the time metadata asset with some or all othermetadata assets in the metadata collection and excluding any metadataasset in the metadata collection that is not an auxiliary primitivemetadata asset until one or more auxiliary primitive metadata assets arefound. For this example, the located auxiliary primitive metadata assetsinclude the auxiliary primitive metadata asset that represents theworking condition of the image sensor used to capture the DA

For one embodiment, the auxiliary inferred metadata asset(s) in themetadata collection may be determined or inferred based on the auxiliaryprimitive metadata asset(s), the primary primitive metadata asset(s),and/or the primary inferred metadata asset(s) in the same metadatacollection. For one embodiment, the auxiliary inferred metadata asset(s)in the metadata collection is determined by clustering auxiliaryprimitive metadata asset(s), the primary primitive metadata asset(s),and/or the primary inferred metadata asset(s) in the same metadatacollection with contextual or other information received from othersources. For example, clustering multiple geo-position metadata assetsin a metadata collection with information from a geographic map receivedfrom a map application can be used determine a geolocation metadataasset. For another embodiment, the auxiliary inferred metadata asset(s)in the metadata collection may be determined by cross-referencing theauxiliary primitive metadata asset(s), the primary primitive metadataasset(s), and/or the primary inferred metadata asset(s) in the samemetadata collection with some or all other metadata assets in the samemetadata collection and excluding any metadata asset in the metadatacollection that is not an auxiliary inferred metadata asset(s) until oneor more auxiliary inferred metadata assets are found. It is to beappreciated that the two embodiments can be combined.

Operation 300 can proceed to blocks 305A-B where, a metadata network isgenerated. At blocks 305A-B, the generated metadata network can be amultidimensional network that includes nodes and edges. For oneembodiment, and with specific regard to block 305A, each node representsan auxiliary inferred metadata asset, an auxiliary primitive metadataasset, a primary primitive metadata asset, or a primary inferredmetadata asset (i.e., a moment). For another embodiment of block 305A,each node representing a primary inferred metadata asset may bedesignated as a moment node. At block 305B, the metadata network candetermine and generate an edge for one or more pairs of nodes. For oneembodiment, each edge indicates a correlation between its pair ofmetadata assets (i.e., nodes).

FIGS. 3B-3C illustrate, in flowchart form, an operation 350 to generatean exemplary metadata network for DAM in accordance with an embodiment.FIGS. 3B-3C provide additional details about the operation 300illustrated in FIG. 3A. Operation 350 can be performed by a DAMlogic/module (e.g., the module/logic 140 described above in connectionwith FIGS. 1A-1B). For one embodiment, portions of the operation 300 and350 may be combined or omitted as desired.

Referring now to FIG. 3B, operation 350 begins at block 347 and proceedsto block 349, where a metadata collection associated with a DAcollection is obtained or received. Block 349 in FIG. 3B is similar toor the same as block 301 in FIG. 3A, which is described above inconnection with FIG. 3A. For brevity, this block is not described again.

As shown in FIGS. 3B-3C, there can be N number of groups, where N refersto the number of one or more DAs in the collection having their owndistinct primary inferred metadata asset (i.e., moment node). For oneembodiment, each group of blocks 351A-N, 353A-N, 355A-N, 357A-N, and359A-N may be performed in parallel (as opposed to sequentially). Forexample, the group of blocks 351A, 353A, 355A, 357A, and 359A may beperformed in parallel with the group of 351N, 353N, 355N, 357N, and359N. Furthermore, performing the groups of blocks in parallel does notmean that each group (e.g., the group of 351A, 353A, 355A, 357A, and359A, etc.) begins and/or ends at the same time as another group (e.g.,the group of 351B, 353B, 355B, 357B, and 359B, etc.). In addition, thetime taken to complete each group (e.g., the group of 351A, 353A, 355A,357A, and 359A, etc.) can be different from the time taken to completeanother group (e.g., the group of 351B, 353B, 355B, 357B, and 359B,etc.). For brevity, only the group of 351A, 353A, 355A, 357A, and 359Awill be discussed below in connection with FIGS. 3B-3C.

Referring again to FIG. 3B, operation 350 proceeds to blocks 351A. Atthis block, a DAM module/logic performing operation 350 identifies oneor more first primary primitive metadata assets. For one embodiment, thefirst primary primitive metadata asset(s) may be selected from themetadata collection that is obtained/received in block 349. Primaryprimitive metadata is described above in connection with FIGS. 1A-2.

Next, operation 350 proceeds to block 353A in FIG. 3B. Here, a DAMmodule/logic performing operation 350 determines a first primaryinferred metadata asset (i.e., the first event metadata asset)associated with one or more first DAs based on the first primaryprimitive metadata asset(s) associated with the one or more first DAs.Primary inferred metadata is described above in connection with FIGS.1A-2. Operation 350 proceeds to block 355A in FIG. 3B, where a firstmoment node is generated based on the first primary inferred metadataasset (e.g., the first event metadata asset, etc.).

Referring now to FIG. 3C, process 350 proceeds to block 357A. Here, oneor more first auxiliary primitive metadata assets are determined orinferred from the metadata collection associated with the DA collection.For one embodiment, block 357A is performed in accordance with one ormore of FIGS. 1-3B, which are described above.

At block 359A, one or more first auxiliary inferred metadata assets maybe determined or inferred based on the first auxiliary primitivemetadata asset(s), the first primary primitive metadata asset(s), and/orthe first primary inferred metadata asset. Next, operation 350 proceedsto block 361. Here, a DAM module/logic performing operation 350 maygenerate a node for each primary primitive metadata asset, eachauxiliary primitive metadata asset, and each auxiliary inferred metadataasset. That is, for each Nth group, a node may be generated for eachprimary primitive metadata asset, each auxiliary primitive metadataasset, and each auxiliary inferred metadata asset. Also, at block 363 ofFIG. 3C, an edge representing a correlation between two metadata assets(i.e., two nodes) may be determined and generated. For one embodiment,the edge is determined and generated as described in connection with atleast FIG. 1B and FIG. 3D. For one embodiment, operation 350 isperformed iteratively and ends at block 365 after no additional nodescan be generated and no additional edges can be generated.

FIG. 3D illustrates, in flowchart form, an operation 390 to generate oneor more edges between nodes in a metadata network for DAM in accordancewith an embodiment. FIG. 3D provides additional details about the block363 of operation 350 described above in connection with FIGS. 3B-3C.Operation 390 can be performed by a DAM logic/module (e.g., themodule/logic 140 described above in connection with FIGS. 1A-1B). Forone embodiment, operation 390 begins at block 391 and proceeds to blocks393A-N, where N refers to the number of one or more DAs in the DAcollection having their own distinct primary inferred metadata asset(i.e., moment node). For brevity, only block 393A is described below inconnection with FIG. 3C. Block 393A requires determining confidenceweights and relevance weights for each of the first primitive metadataassets (i.e., the primary primitive metadata asset(s) and the auxiliaryprimitive metadata asset(s), etc.) and each of the first inferredmetadata assets (i.e., the primary inferred metadata asset and theauxiliary inferred metadata asset(s), etc.). Confidence weights andrelevance weights are described above in connection with one or more ofFIGS. 1A-3B.

At block 395 of FIG. 3D, a DAM logic/module performing operation 390 maydetermine, for each pair of nodes, whether a correlation exists betweenthe two nodes. For one embodiment, this determination includesdetermining that a set of two nodes is correlated when at least one ofthe following occurs: (i) the confidence weight between the two nodesexceeds a threshold confidence; (ii) the relevance weight between the atleast two nodes exceeds a threshold relevance; or (iii) a combination ofthe confidence weight and the relevance weight exceeds a thresholdcorrelation. Combinations of the confidence and relevance weightsinclude, but are not limited to, a sum of the two weights, a product ofthe two weights, an average of the two weights, a median of the twoweights, and a difference between the two weights. Next, operation 390proceeds to block 397, where a DAM logic/module performing operation 390generates an edge between the correlated nodes in the multidimensionalnetwork representing the KB. For one embodiment, operation 390 isperformed iteratively and ends at block 399 when no more additionaledges can be generated between two nodes.

One or more of operations 300, 350, and 390 described above inconnection with FIGS. 3A-3D, respectively can be used to update themetadata network 175 described above in connection with FIGS. 1A-2. Forexample, a DAM module/logic 140 updates the metadata network 175 usingone or more of operations 300, 350, and 390 as the DAM module/logic 140obtains or receives new primitive metadata 170 and/or as the DAMmodule/logic 140 determines or infers new inferred metadata 170 based onthe new primitive metadata 170.

Referring now to FIG. 4, which is a flowchart representing oneembodiment of an operation 400 to relate and/or present at least twodigital assets (DAs) from a collection of DAs (DA Collection) in accordwith one embodiment. Operation 400 can be performed by a DAMlogic/module (e.g., the module/logic 140 described above in connectionwith FIGS. 1A-1B). Operation 400 begins at block 401, where a metadatanetwork is obtained or received as described above in connection withFIGS. 1A-3C.

Operation 400 proceeds to block 403, where a DAM logic/module performingoperation 400 may select a first metadata asset that is represented as anode in the metadata network. The first metadata asset may be anon-moment node or a moment node. For one embodiment, the first metadataasset (i.e., the selected node) can represent a primary primitivemetadata asset, a primary inferred metadata asset, an auxiliaryprimitive metadata asset, or an auxiliary inferred metadata assetassociated one or more DAs in a DA collection. For example, when a useris consuming or perceiving a DA (e.g., a single DA, a group of DAs,etc.) via an output device (e.g., a display device, an audio outputdevice, etc.), then a user-input indicating a selection of the DA cantrigger a selection of a specific metadata asset associated with the DAin the metadata network. Alternatively, or additionally, a userinterface may be provided to the user to enable the user to select aspecific metadata asset associated with one or more DAs from a group ofmetadata assets associated with the one or more DAs. Exemplary userinterfaces include, but are not limited to, graphical user interfaces,voice user interfaces, object-oriented user interfaces, intelligent userinterfaces, hardware interfaces, touch user interfaces, touchscreendevices or systems, gesture interfaces, motion tracking interfaces, andtangible user interfaces. The user interface may be presented to theuser in response to the user selecting the specific DA. One or morespecific examples of a user interface can be found in U.S. ProvisionalPatent Application No. 62/349,109, entitled “USER INTERFACES FORRETRIEVING CONTEXTUALLY RELEVANT MEDIA CONTENT,” Docket No. 770003002400(P31183USP1), filed Jun. 12, 2016, which is incorporated by reference inits entirety.

For one embodiment, operation 400 includes block 405. At this block, adetermination may be made that the first metadata asset (i.e., theselected node) is associated with a second metadata asset that isrepresented as a second node in the metadata network. The second nodecan be a moment node or a non-moment node. For example, the secondmetadata asset can be a first moment node. For this example, thedetermination may include determining that at least one of the primaryprimitive metadata asset(s), the auxiliary primitive metadata asset(s),or the auxiliary inferred metadata asset(s) represented by the selectednode (i.e., the first metadata asset) corresponds to the second metadataasset (i.e., the first moment node).

At block 407, a third metadata asset can be identified based on thefirst metadata asset (i.e., the selected node) and/or the secondmetadata asset (i.e., the second node). The third metadata asset can berepresented as a third node in the metadata network. The third node maybe a moment node or a non-moment node. For example, the third metadataasset can be represented as a second moment node that is different fromthe first moment node in the immediately preceding example (i.e., thesecond metadata asset). At block 409, at least one DA associated withthe third metadata asset (e.g., the second moment node in the metadatanetwork, etc.) may be presented via an output device. In this way,operation 400 can assist with relating and presenting one or more DAs ina DA collection based on their metadata.

FIG. 5 is a flowchart representing an operation 500 to determine andpresent at least two digital assets (DAs) from a DA collection based ona predetermined criterion in accordance with one embodiment. A DAMlogic/module can perform operation 500 (e.g., the module/logic 140described above in connection with FIGS. 1A-1B, etc.). For oneembodiment, a DAM logic/module performs operation 500 to determineand/or present one or more DAs based on a predetermined criterion andone or more notable moments (i.e., one or more event metadata assets).For example, if the predetermined criterion requires a date from one ormore previous years that share the same day as today, then a DAMlogic/module performs operation 500 to determine and/or present one ormore DAs associated with one or more notable moments (i.e., one or moreevent metadata assets) that share the same day as today. For oneembodiment, the predetermined criterion includes contextual information.

Operation 500 begins at block 501, where a DAM logic/module performingoperation 500 obtains or receives a metadata network. One or moreembodiments of metadata networks are described above in connection withFIGS. 1A-4. At block 503, a predetermined criterion is received. For oneembodiment, the predetermined criterion may be based on contextualinformation. Context and contextual information are described above.Process 500 proceeds to block 505, where a DAM logic/module performingoperation 500 may determine that one or more metadata assets that arerepresented as nodes in the metadata network satisfy the predeterminedcriterion. The nodes that satisfy the predetermined criterion can bemoment nodes or non-moment nodes. For one embodiment, the identifiednodes match the criterion. For example, the predetermined criterion caninclude a geolocation that will be visited by a user during a futuretime period. Thus, for this example, one or more nodes that include thegeolocation specified by the predetermined criterion can be identifiedin the metadata network.

For one embodiment, the predetermined criterion can be based on one ormore metadata assets that represent a break in a user's habits. For thisembodiment, the predetermined criterion can be determined by identifyingone or more metadata assets having a low rate of occurrence based on ananalysis of metadata assets of that metadata type. For example, a countand/or comparison of all time metadata assets in a metadata collectionreveals that the lowest number of time metadata assets are those havingtimes between 12:00 AM and 5:00 AM every day. Consequently, and for thisexample, the times between 12:00 AM and 5:00 AM every day can bespecified as the predetermined criterion. Using the predeterminedcriterion described above to identify a break in a user's habits canidentify metadata assets associated with one or more interesting DAs(e.g., one or more images that represent a break in a user's dailyroutine, etc.). Exemplary predetermined criterion representing a breakin a user's habits include, but are not limited to, visiting ageolocation that has never been visited before (e.g., a first day inHawaii, etc.), visiting a geolocation that has not been visited in anextended time (e.g., a trip to your birthplace after being away for morethan a month, a year, 6 months, etc.), and an outing with one or moreidentified persons that have not been interacted with for an extendedtime (e.g., a dinner with childhood friends you haven't seen in over amonth, a year, 6 months, etc.).

Operation 500 proceeds to block 507. At this block, a determination maybe made that the identified metadata data asset(s), which arerepresented as node(s) in the metadata network, are associated with oneor more other metadata data asset(s). These other metadata asset(s)could be moment nodes or non-moment nodes that are represented in themetadata network. For one embodiment, the identified node(s) in block505 can be used to determine one or more moment nodes in block 507. Forexample, one of the identified node(s) in block 505 can represent ametadata asset that describes a geolocation to be attended by the user.Thus, for this example, one or more moments nodes that represent eventmetadata asset(s) associated with the geolocation specified by apredetermined criterion can be determined in the metadata network atblock 507. The determined metadata asset(s) in block 507 can be used toidentify one or more DAs in the DA collection. At block 509, theidentified DA(s) associated with the determined metadata asset(s) inblock 507 can be presented via an output device (e.g., a display device,an audio output device, etc.) for consumption by a user of the device.

FIG. 6 is a flowchart representing an operation 600 to determine andpresent a representative set of digital assets (DAs) for a momentaccording to one embodiment. For one embodiment, operation 600 isperformed on metadata assets associated with a group of DAs that sharethe same event metadata. Thus, for this embodiment, the metadatanetworks described above are not always required. Other embodiments,however, perform operation 600 on one or more moment nodes in a metadatanetwork. For brevity, operation 600 will be described in connection witha moment (i.e., an event metadata asset) in a metadata network.

Operation 600 can be performed by a DAM logic/module to curate one ormore representative DAs associated with an event metadata asset that isrepresented as a moment node in a metadata network. As used herein,“curation” and its variations refer to determining and/or presenting arepresentative set of DAs for summarizing the one or more DAs associatedwith a moment. For example, if there are fifty images associated with amoment, then a curation of the moment can include determining and/orpresenting ten images summarizing the fifty DAs associated with themoment.

Operation 600 begins at block 605, where a DAM logic/module performingoperation 600 obtains or receives a maximum number of DAs to be used forrepresenting the DAs associated with a moment (i.e., an event metadataasset) that is represented as a moment node in a metadata network and aminimum number of DAs to be used for representing the DAs associatedwith the moment (i.e., the event metadata asset) that is represented asthe moment node in the metadata network. For one embodiment, the maximumand minimum numbers can be received via user input provided through aninput device (e.g., peripheral(s) 190 described above in connection withFIG. 1A, input device(s) 706 described below in connection with FIG. 7,etc.). For another embodiment, the maximum and minimum numbers can bepredetermined numbers that are applied automatically by the DAMlogic/module performing operation 600. These predetermined numbers canbe set when developing the DAM logic/module that performs operation 600or through an input provided via a user interface (e.g., through a userpreferences setting, etc.). For one embodiment, the maximum and minimumnumbers can be determined dynamically based on processing operationsperformed by computational resources associated with the DAMlogic/module. For example, as more computational resources becomeavailable, the maximum and minimum numbers can be increased ordecreased.

At block 607, one or more other metadata assets associated with theselected moment may be identified and further classified into multiplesub-clusters. The one or more other metadata assets may include primaryprimitive metadata assets, auxiliary primitive metadata assets, and/orauxiliary inferred metadata assets that correspond to the moment (i.e.,the event metadata asset) that is represented as the moment node in themetadata network. For one embodiment, the one or more other metadataassets are identified using their corresponding nodes in the metadatanetwork. For one embodiment, block 607 also includes determining a timeperiod spanned by the other metadata assets associated with the selectedmoment and determining whether this time period is greater than or equalto a predetermined threshold. This predetermined threshold is used todifferentiate collections of metadata assets that represent a shortmoment (e.g., a birthday party spanning three hours, etc.) fromcollections of metadata assets that represent a longer moment (e.g., avacation trip spanning a week, etc.). Curation settings can be used toselect representative DAs for collections of metadata assets thatrepresent longer moments. When the time period spanned by the othermetadata assets associated with the selected moment is greater than orequal to a predetermined threshold, the other metadata assets associatedwith the selected moment may be considered a dense cluster.Alternatively, when a time period spanned by the other metadata assetsassociated with the selected moment fails to exceed the predeterminedthreshold, the other metadata assets associated with the selected momentmay be considered a diffused or sparse cluster. For one embodiment, whena dense cluster is determined, operation 500 (as described above) may beused to select and present the DAs associated with selected moment viaan output device. In contrast, when a diffused or sparse cluster isdetermined, the other metadata assets associated with the selectedmoment may be ordered sequentially. For one embodiment, sequentiallyordering the other metadata assets may be based on at least one acapture time, a modification time, or a save time. After the othermetadata assets associated with the selected moment are ordered, block607 includes applying a clustering technique based on time and spatialdistances between the selected moment's metadata assets (i.e., the othermetadata assets). Examples of such clustering techniques include, butare not limited to, exclusive clustering algorithms, overlappingclustering algorithms, hierarchical clustering, and probabilisticclustering algorithms. For one embodiment, time may be the base vectorused for the clustering technique and the spatial distances between theselected moment's metadata assets may be a function of the time.

For one embodiment, block 607 may include iteratively applying a firstdensity-based data clustering algorithm to the results of the clusteringtechnique described above. For one embodiment, the first density-baseddata clustering algorithm includes the “density-based spatial clusteringof applications with noise” or DBSCAN algorithm. For one embodiment, theDBSCAN algorithm may be applied to determine or infer sub-clusters ofthe selected moment's metadata assets while avoiding outlier metadataassets. Such outliers typically lie in low density regions. For oneembodiment, block 607 may also include applying a second density-baseddata-clustering algorithm to the results of the first density-baseddata-clustering algorithm. For one embodiment, the second density-baseddata-clustering algorithm can include the “ordering points to identifythe clustering structure” or OPTICS algorithm. For one embodiment, theOPTICS algorithm may be applied to results of the DBSCAN algorithm todetect meaningful sub-clusters of the other metadata assets associatedwith the selected moment. The OPTICS algorithm linearly orders the othermetadata assets associated with the selected moment such that metadataassets that are spatially closest to each other become neighbors.Additionally, a special distance may be stored for each sub-cluster ofthe other metadata assets. This special distance can represent themaximum spatial distance between two metadata assets that needs to beaccepted for a sub-cluster in order to have two or more metadata assetsbe deemed as belonging to that sub-cluster. That is, any two metadataassets whose spatial distance exceeds the special distance are notconsidered part of the same sub-cluster. For one embodiment, block 607also includes applying a weight to each metadata asset in eachsub-cluster that results from applying the OPTICS algorithm. Forexample, the weight can be a score between 0.0 and 1.0, where eachmetadata asset in each sub-cluster has a starting score of 0.5. Block607 may further include applying at least one heuristic function todetermine a representative weight for each determined sub-cluster basedon the individual weights within each sub-cluster.

Operation 600 proceeds to block 609, where metadata assets are selectedfrom the identified sub-cluster(s). The selected metadata assetscorrespond to or identify the representative DAs. For one embodiment,block 609 includes applying an adaptive election algorithm to select orfilter a sub-set of the sub-clusters determined in block 607. The numberof sub-clusters in the sub-set may be equal to the maximum numberdescribed above in connection with block 605. Block 609 can also includedetermining a percentage of representative DAs that can be contributedby each sub-cluster in the sub-set to the maximum number described abovein connection with block 605. For example, if there are two sub-clustersin the sub-set and the first sub-cluster has metadata assets associatedwith 20 DAs while the second sub-cluster has metadata assets associatedwith 10 DAs, then the first sub-cluster can contribute 75% of its DAs tothe maximum number of representative DAs and the second sub-cluster cancontribute 25% of its DAs to the maximum number of representative DAs.For one embodiment, when the number of representative DAs a sub-clustercan contribute to the representative DAs is less than the minimum numberdescribed above in connection with block 605, that sub-cluster may beremoved from consideration. Thus, and with regard to the immediatelypreceding example, if 25% of the DAs that can be contributed by thesecond sub-cluster is less than the minimum number described above inconnection with block 605, then the second sub-cluster may be removedfrom consideration. For one embodiment, determining the maximum numberthat each sub-cluster in the sub-set can contribute to the number ofrepresentative DAs may be performed iteratively until each sub-clustercan contribute at least the minimum number described above in connectionwith block 605.

At block 609, hierarchical cluster analysis (e.g., agglomerativeclustering, divisive clustering, etc.) can be performed on thesub-clusters that can contribute a number of their DAs to therepresentative DAs. Exemplary agglomerative clustering techniquesinclude, but are not limited to, hierarchical agglomerative clustering(HAC) techniques. Exemplary divisive clustering techniques include, butare not limited to, k-mean clustering techniques (where k is equal tothe number of DAs associated with a sub-cluster that can be contributedto the total number of representative DAs and where k is at least equalto the minimum number described above in connection with block 605). Forone embodiment, the selected metadata assets associated with DAs in asub-cluster that can be contributed to the total number ofrepresentative DAs are then filtered for redundancies and noise. Here,noisy metadata assets may be assets that have incomplete information orare otherwise not associated with the selected moment. After theredundant and noisy metadata assets are removed, the DAs associated withthe unremoved metadata assets may be deemed the total number ofrepresentative DAs. For one embodiment, this total number of the one ormore representative DAs is (i) less than or equal to the maximum numberfrom block 605 and (ii) greater than or equal to the minimum number fromblock 605. As shown in block 611, the DAs associated with the unremovedmetadata assets can be presented on an output device as therepresentative DAs.

FIG. 7 is a block diagram illustrating an exemplary data processingsystem 700 that may be used with one or more of the describedembodiments. For example, the system 700 may represent any dataprocessing system (e.g., one or more of the systems described aboveperforming any of the operations or methods described above inconnection with FIGS. 1A-6, etc.). System 700 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of acomputer system, or as components otherwise incorporated within achassis of a computer system. Note also that system 700 is intended toshow a high-level view of many, but not all, components of the computersystem. Nevertheless, it is to be understood that additional componentsmay be present in certain implementations and furthermore, differentarrangements of the components shown may occur in other implementations.System 700 may represent a desktop computer system, a laptop computersystem, a tablet computer system, a server computer system, a mobilephone, a media player, a personal digital assistant (PDA), a personalcommunicator, a gaming device, a network router or hub, a wirelessaccess point (AP) or repeater, a set-top box, or a combination thereof.Further, while only a single machine or system is illustrated, the term“machine” or “system” shall also be taken to include any collection ofmachines or systems that individually or jointly execute instructions toperform any of the methodologies discussed herein.

For one embodiment, system 700 includes processor(s) 701, memory 703,devices 705-709, and device 711 via a bus or an interconnect 710. System700 also includes a network 712. Processor(s) 701 may represent a singleprocessor or multiple processors with a single processor core ormultiple processor cores included therein. Processor(s) 701 mayrepresent one or more general-purpose processors such as amicroprocessor, a central processing unit (CPU), graphics processingunit (GPU), or the like. More particularly, processor(s) 701 may be acomplex instruction set computer (CISC), a reduced instruction setcomputer (RISC) or a very long instruction word (VLIW) computerarchitecture processor, or processors implementing a combination ofinstruction sets. Processor(s) 701 may also be one or morespecial-purpose processors such as an application specific integratedcircuit (ASIC), an application-specific instruction set processor(ASIP), a cellular or baseband processor, a field programmable gatearray (FPGA), a digital signal processor (DSP), a physics processingunit (PPU), an image processor, an audio processor, a network processor,a graphics processor, a graphics processing unit (GPU), a networkprocessor, a communications processor, a cryptographic processor, aco-processor, an embedded processor, a floating-point unit (FPU), or anylogic that can process instructions.

Processor(s) 701, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor(s) can be implemented as one or moresystem-on-chip (SoC) integrated circuits (ICs). A digital assetmanagement (DAM) logic/module 728A may reside, completely or at leastpartially, within processor(s) 701. In one embodiment, the DAMlogic/module 728A enables the processor(s) 701 to perform any or all ofthe operations or methods described above in connection with FIGS. 1A-6.Additionally or alternatively, the processor(s) 701 may be configured toexecute instructions for performing the operations and methodologiesdiscussed herein.

System 700 may further include a graphics interface that communicateswith optional graphics subsystem 704, which may include a displaycontroller, a graphics processing unit (GPU), and/or a display device.Processor(s) 701 may communicate with memory 703, which in oneembodiment can be implemented via multiple memory devices to provide fora given amount of system memory. Memory 703 may include one or morevolatile storage (or memory) devices such as random access memory (RAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), orother types of storage devices. Memory 703 may store informationincluding sequences of instructions that are executed by processor(s)701 or any other device. For example, executable code and/or data from avariety of operating systems, device drivers, firmware (e.g., inputoutput basic system or BIOS), and/or applications can be loaded inmemory 703 and executed by processor(s) 701. An operating system can beany kind of operating system. A DAM logic/module 728D may also reside,completely or at least partially, within memory 703.

For one embodiment, the memory 703 includes a DAM logic/module 728B asexecutable instructions. For another embodiment, when the instructionsrepresented by DAM logic/module 728B are executed by the processor(s)701, the instructions cause the processor(s) 701 to perform any, all, orsome of the operations or methods described above in connection withFIGS. 1A-6.

System 700 may further include I/O devices such as devices 705-708,including network interface device(s) 705, optional input device(s) 706,and other optional I/O device(s) 707. Network interface device 705 mayinclude a wired or wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 706 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 704), a pointerdevice such as a stylus, and/or a keyboard (e.g., a physical keyboard ora virtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 706 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or a break thereof using one ormore touch sensitivity technologies, including but not limited tocapacitive, resistive, infrared, and surface acoustic wave technologies,as well as other proximity sensor arrays or other elements fordetermining one or more points of contact with the touch screen.

I/O devices 707 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other I/O devices 707 may include universal serialbus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Device(s) 707 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 710 via a sensor hub (not shown),while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 700.

To provide for persistent storage for information such as data,applications, one or more operating systems and so forth, a mass storagedevice or devices (not shown) may also coupled to processor(s) 701. Forvarious embodiments, to enable a thinner and lighter system design aswell as to improve system responsiveness, this mass storage may beimplemented via a solid state device (SSD). However in otherembodiments, the mass storage may primarily be implemented using a harddisk drive (HDD) with a smaller amount of SSD storage to act as a SSDcache to enable non-volatile storage of context state and other suchinformation during power down events so that a fast power up can occuron re-initiation of system activities. In addition, a flash device maybe coupled to processor(s) 701, e.g., via a serial optional peripheralinterface (SPI). This flash device may provide for non-volatile storageof system software, including a basic input/output software (BIOS) andother firmware.

A DAM logic/module 728C may be part of a specialized stand-alonecomputing system/device 711 that is formed from hardware, software, or acombination thereof. For one embodiment, the DAM logic/module 728Cperforms any, all, or some of the operations or methods described abovein connection with FIGS. 1A-6.

Storage device 708 may include computer-accessible storage medium 709(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions orsoftware—e.g., a DAM logic/module 728D.

For one embodiment, the instruction(s) or software stored on storagemedium 709 embody one or more methodologies or functions described abovein connection with FIGS. 1A-6. For another embodiment, the storagedevice 708 includes a DAM logic/module 728D as executable instructions.When the instructions represented by a DAM logic/module 728D areexecuted by the processor(s) 701, the instructions cause the system 700to perform any, all, or some of the operations or methods describedabove in connection with FIGS. 1A-6.

Computer-readable storage medium 709 can store some or all of thesoftware functionalities of a DAM logic/module 728A-D described abovepersistently. While computer-readable storage medium 709 is shown in anexemplary embodiment to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterms “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing or encoding a set of instructionsfor execution by the system 700 and that cause the system 700 to performany one or more of the disclosed methodologies. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Note that while system 700 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as such,details are not germane to the embodiments described herein. It willalso be appreciated that network computers, handheld computers, mobilephones, servers, and/or other data processing systems, which have fewercomponents or perhaps more components, may also be used with theembodiments described herein.

In the foregoing description, numerous specific details are set forth,such as specific configurations, dimensions and processes, etc., inorder to provide a thorough understanding of the embodiments. In otherinstances, well-known processes and manufacturing techniques have notbeen described in particular detail in order to not unnecessarilyobscure the embodiments. Reference throughout this specification to “oneembodiment,” “an embodiment,” “another embodiment,” “other embodiments,”“some embodiments,” and their variations means that a particularfeature, structure, configuration, or characteristic described inconnection with the embodiment is included in at least one embodiment.Thus, the appearances of the phrase “for one embodiment,” “for anembodiment,” “for another embodiment,” “in other embodiments,” “in someembodiments,” or their variations in various places throughout thisspecification are not necessarily referring to the same embodiment.Furthermore, the particular features, structures, configurations, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

In the following description and claims, the terms “coupled” and“connected,” along with their derivatives, may be used. It should beunderstood that these terms are not intended as synonyms for each other.“Coupled” is used to indicate that two or more elements or components,which may or may not be in direct physical or electrical contact witheach other, co-operate or interact with each other. “Connected” is usedto indicate the establishment of communication between two or moreelements or components that are coupled with each other.

Some portions of the preceding detailed description have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities. Unless specifically stated otherwise asapparent from the above discussion, it is appreciated that throughoutthe description, discussions utilizing terms such as those set forth inthe claims below, refer to the action and processes of a computersystem, or similar electronic computing system, that manipulates andtransforms data represented as physical (electronic) quantities withinthe computer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Embodiments described herein can relate to an apparatus for performing acomputer program (e.g., the operations described herein, etc.). Such acomputer program is stored in a non-transitory computer readable medium.A machine-readable medium includes any mechanism for storing informationin a form readable by a machine (e.g., a computer). For example, amachine-readable (e.g., computer-readable) medium includes a machine(e.g., a computer) readable storage medium (e.g., read only memory(“ROM”), random access memory (“RAM”), magnetic disk storage media,optical storage media, flash memory devices).

Although operations or methods are described above in terms of somesequential operations, it should be appreciated that some of theoperations described may be performed in a different order. Moreover,some operations may be performed in parallel rather than sequentially.Embodiments described herein are not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the variousembodiments of the disclosed subject matter. In utilizing the variousaspects of the embodiments described herein, it would become apparent toone skilled in the art that combinations, modifications, or variationsof the above embodiments are possible for managing components of aprocessing system to increase the power and performance of at least oneof those components. Thus, it will be evident that various modificationsmay be made thereto without departing from the broader spirit and scopeof at least one of the disclosed concepts set forth in the followingclaims. The specification and drawings are, accordingly, to be regardedin an illustrative sense rather than a restrictive sense.

In the development of any actual implementation of one or more of thedisclosed concepts (e.g., such as a software and/or hardware developmentproject, etc.), numerous decisions must be made to achieve thedevelopers' specific goals (e.g., compliance with system-relatedconstraints and/or business-related constraints). These goals may varyfrom one implementation to another, and this variation could affect theactual implementation of one or more of the disclosed concepts set forthin the embodiments described herein. Such development efforts might becomplex and time-consuming, but may still be a routine undertaking for aperson having ordinary skill in the art in the design and/orimplementation of one or more of the inventive concepts set forth in theembodiments described herein.

One aspect of the present technology is the gathering and use of dataavailable from various sources to improve the operation of the metadatanetwork. The present disclosure contemplates that in some instances,this gathered data may include personal information data that uniquelyidentifies a specific person. Such personal information data can includedemographic data, location-based data, telephone numbers, emailaddresses, twitter ID's, home addresses, or any other identifyinginformation.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used toimprove the metadata assets and enable identifying correlation betweenmetadata nodes. Further, other uses for personal information data thatbenefit the user are also contemplated by the present disclosure.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof the present metadata network, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data for use asmetadata assets in the metadata network.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data.

As used in the description above and the claims below, the phrase “atleast one of A, B, or C” includes A alone, B alone, C alone, acombination of A and B, a combination of B and C, a combination of A andC, and a combination of A, B, and C. That is, the phrase “at least oneof A, B, or C” means A, B, C, or any combination thereof such that oneor more of a group of elements consisting of A, B and C, and should notbe interpreted as requiring at least one of each of the listed elementsA, B and C, regardless of whether A, B and C are related as categoriesor otherwise. Furthermore, the use of the article “a” or “the” inintroducing an element should not be interpreted as being exclusive of aplurality of elements. Also, the recitation of “A, B and/or C” is equalto “at least one of A, B or C.”

Also, the use of “a” refers to “one or more” in the present disclosure.For example, “a DA” refers to “one or more DAs.”

What is claimed is:
 1. A computer-implemented method for relating atleast two digital assets using digital asset management, comprising:obtaining, by a processor, a metadata network associated with acollection of digital assets (DA collection), wherein the metadatanetwork comprises correlated metadata assets, wherein each metadataasset is represented by a node in the metadata network that describes acharacteristic associated with one or more digital assets (DAs) in theDA collection, and wherein a correlation between at least two metadataassets is represented as an edge between the nodes representing the atleast two metadata assets; selecting a first metadata asset in themetadata network, the first metadata asset being associated with a firstplurality of DAs in the DA collection; determining that the firstmetadata asset is associated with a second metadata asset, wherein thesecond metadata asset describes an event associated with the firstplurality of DAs; identifying a third metadata asset in the metadatanetwork based on one or more of the first metadata asset or the secondmetadata asset, the third metadata asset being associated with a secondplurality of DAs in the DA collection; and causing, by the processor,the second plurality of DAs to be presented via an output device.
 2. Thecomputer-implemented method of claim 1, wherein selecting the firstmetadata asset is performed in response to the processor receiving inputvia an input device.
 3. The computer-implemented method of claim 1,wherein identifying the third metadata asset includes: determining oneor more correlations between one or more fourth metadata assets and atleast one of the first metadata asset or second metadata asset; anddetermining one or more correlations between the one or more fourthmetadata assets and the third metadata asset.
 4. Thecomputer-implemented method of claim 1, wherein the third metadata assetdescribes a second event associated with the second plurality of DAs. 5.The computer-implemented method of claim 1, wherein the third metadataasset does not describe an event associated with the second plurality ofDAs.
 6. The computer-implemented method of claim 1, wherein each DA isan image.
 7. The computer-implemented method of claim 1, whereinidentifying the third metadata asset includes: determining contextualinformation associated with at least one of the first metadata asset orsecond metadata asset; and determining one or more correlations betweenthe contextual information and the third metadata asset.
 8. Anon-transitory computer readable medium comprising instructions forrelating at least two digital assets using digital asset management,which when executed by one or more processors, cause the one or moreprocessors to: obtain a metadata network associated with a collection ofdigital assets (DA collection), wherein the metadata network comprisescorrelated metadata assets, wherein each metadata asset is representedby a node in the metadata network that describes a characteristicassociated with one or more digital assets (DAs) in the DA collection,and wherein a correlation between at least two metadata assets isrepresented as an edge between the nodes representing the at least twometadata assets; select a first metadata asset in the metadata network,the first metadata asset being associated with a first plurality of DAsin the DA collection; determine that the first metadata asset isassociated with a second metadata asset, wherein the second metadataasset describes an event associated with the first plurality of DAs;identify a third metadata asset in the metadata network based on one ormore of the first metadata asset or the second metadata asset, the thirdmetadata asset being associated with a second plurality of DAs in the DAcollection; and cause the second plurality of DAs to be presented via anoutput device.
 9. The non-transitory computer readable medium of claim8, wherein the instructions that cause the one or more processors toselect the node associated with the DA include one or more instructionsthat cause the one or more processors to: select the first metadataasset is performed in response to the processor receiving input via aninput device.
 10. The non-transitory computer readable medium of claim8, wherein the instructions that cause the one or more processors toidentify the third metadata asset include one or more instructions thatcause the one or more processors to: determine one or more correlationsbetween one or more fourth metadata assets and at least one of the firstmetadata asset or second metadata asset; and determine one or morecorrelations between the one or more fourth metadata assets and thethird metadata asset.
 11. The non-transitory computer readable medium ofclaim 8, wherein the third metadata asset describes a second eventassociated with the second plurality of DAs.
 12. The non-transitorycomputer readable medium of claim 8, wherein the third metadata assetfails to describe an event associated with the second plurality of DAs.13. The non-transitory computer readable medium of claim 8, wherein eachDA is an image.
 14. The non-transitory computer readable medium of claim8, wherein the instructions that cause the one or more processors toidentify the third metadata asset include one or more instructions thatcause the one or more processors to: determine contextual informationassociated with at least one of the first metadata asset or secondmetadata asset; and determine one or more correlations between thecontextual information and the third metadata asset.
 15. A processingsystem for relating at least two digital assets using digital assetmanagement, the processing system comprising: logic configured to:obtain a metadata network associated with a collection of digital assets(DA collection), wherein the metadata network comprises correlatedmetadata assets, wherein each metadata asset is represented by a node inthe metadata network that describes a characteristic associated with oneor more digital assets (DAs) in the DA collection, and wherein acorrelation between at least two metadata assets is represented as anedge between the nodes representing the at least two metadata assets;select a first metadata asset in the metadata network, the firstmetadata asset being associated with a first plurality of DAs in the DAcollection; determine that the first metadata asset is associated with asecond metadata asset, wherein the second metadata asset describes anevent associated with the first plurality of DAs; identify a thirdmetadata asset in the metadata network based on one or more of the firstmetadata asset or the second metadata asset, the third metadata assetbeing associated with a second plurality of DAs in the DA collection;and cause the second plurality of DAs to be presented via an outputdevice.
 16. The system of claim 15, wherein the system further comprisesan input device configured to provide an input to the logic and whereinthe logic being configured to select the node associated with the DAincludes the logic being configured to: select the first metadata assetis performed in receiving the input.
 17. The system of claim 15, whereinthe logic being configured to identify the third metadata asset includesthe logic being configured to: determine one or more correlationsbetween one or more fourth metadata assets and at least one of the firstmetadata asset or second metadata asset; and determine one or morecorrelations between the one or more fourth metadata assets and thethird metadata asset.
 18. The system of claim 15, wherein the thirdmetadata asset describes a second event associated with the secondplurality of DAs.
 19. The system of claim 15, wherein the third metadataasset fails to describe an event associated with the second plurality ofDAs.
 20. The system of claim 15, wherein each DA is an image.
 21. Thesystem of claim 15, wherein the logic being configured to identify thethird metadata asset includes the logic being configured to: determinecontextual information associated with at least one of the firstmetadata asset or second metadata asset; and determine one or morecorrelations between the contextual information and the third metadataasset.